{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "2.heart_disease_diagnosis.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtztNMjHSErp",
        "colab_type": "code",
        "outputId": "bfecd354-4950-415a-a231-e4973621e24e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "!pip install tensorflow-gpu==2.0.0-alpha0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow-gpu==2.0.0-alpha0 in /usr/local/lib/python3.6/dist-packages (2.0.0a0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.7.1)\n",
            "Requirement already satisfied: tb-nightly<1.14.0a20190302,>=1.14.0a20190301 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.14.0a20190301)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (3.7.1)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.7.1)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.33.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.1.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.7)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.14.6)\n",
            "Requirement already satisfied: tf-estimator-nightly<1.14.0.dev2019030116,>=1.14.0.dev2019030115 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.14.0.dev2019030115)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.15.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.1.5)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.9)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.2.2)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.11.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha0) (3.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha0) (0.15.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==2.0.0-alpha0) (40.9.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0-alpha0) (2.8.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wd9UXbwBUxK-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from google.colab import drive\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
        "\n",
        "rcParams['figure.figsize'] = 14, 8\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "\n",
        "np.random.seed(RANDOM_SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hhVnWPzTREN",
        "colab_type": "code",
        "outputId": "ac47f6b5-5c1f-4925-d325-34d335bf668a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "drive.mount(\"/content/drive\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4OrY05Q6Uf6O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "heart_csv_path = \"/content/drive/My Drive/Colab Notebooks/tensorflow-2/data/heart.csv\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1UNIIxr7Uqnn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv(heart_csv_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUU6Eu5UU24t",
        "colab_type": "code",
        "outputId": "2892f2be-2e50-4ea5-cc68-b8f60944323a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp    trestbps        chol         fbs  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
              "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
              "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
              "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
              "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
              "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
              "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
              "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
              "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
              "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
              "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
              "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
              "\n",
              "             thal      target  \n",
              "count  303.000000  303.000000  \n",
              "mean     2.313531    0.544554  \n",
              "std      0.612277    0.498835  \n",
              "min      0.000000    0.000000  \n",
              "25%      2.000000    0.000000  \n",
              "50%      2.000000    1.000000  \n",
              "75%      3.000000    1.000000  \n",
              "max      3.000000    1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LBUy0UWw8fb5",
        "colab_type": "code",
        "outputId": "24dbf55a-d150-48c2-e1f2-f694caa29b43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S3wUHL0E96M6",
        "colab_type": "code",
        "outputId": "fb740dd7-0b47-4e0c-df43-9a058be5356c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "data.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5RQ-xtTBtNec",
        "colab_type": "code",
        "outputId": "105526a7-b01e-439c-93b0-005d9302714c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        }
      },
      "source": [
        "f = sns.countplot(x='target', data=data)\n",
        "f.set_title(\"Heart disease presence distribution\")\n",
        "f.set_xticklabels(['No Heart disease', 'Heart Disease'])\n",
        "plt.xlabel(\"\");"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAAAwIJwAAAAAwIBwAgAAAAADwgkAAAAADJwynNasWaNu3brJz89P\nnTt31pIlS+zr1q9fr549eyogIEARERGaOXOmcnJyKm5YAAAAADc914oe4GobNmzQlClTNGPGDAUH\nB+ubb77RxIkTFRQUpN9++01jx47VG2+8oc6dO+unn35STEyMqlatquHDh1f06AAAAABuUk53xmnO\nnDkaOnSoOnToIDc3N4WEhGjTpk1q1aqVli1bpvDwcHXv3l1ubm7y9fXVoEGDlJSUpNzc3IoeHQAA\nAMBNyqnC6dSpU/rhhx902223qX///mrbtq0efvhhffTRR5KklJQU+fv7O+zj7+8vm82mn3/+uQIm\nBgAAAFAZONWler/88oskadWqVXrjjTfUsGFDrV69Ws8//7zq1aun9PR0eXp6OuxjtVolSenp6Wrc\nuLHxGMnJyaU/OACgSHzfBQBc7Ub82eBU4ZSXlydJioqKkq+vryRp4MCBWrt2rdasWVMqxwgMDCyV\nxyl1a7dW9AQAUCac9vuuk3v30xUVPQIAlBln/tlQVNQ51aV6derUkfR/Z5Hy+fj46OTJk/Ly8pLN\nZnNYl5GRIUny9vYunyEBAAAAVDpOF041a9bUvn37HJYfPnxYDRo0UEBAgFJTUx3WJScny9vbWz4+\nPuU5KgAAAIBKxKnCycXFRYMHD9ayZcv0+eefKysrS8uXL9e3336r/v3764knntDOnTu1ceNGZWVl\nad++fVq8eLEGDx4si8VS0eMDAAAAuEk51WecJOnJJ5/U5cuXNW7cOJ05c0aNGjXSggUL1Lx5c0nS\njBkzFB8fr9jYWHl5eSkqKkpDhgyp4KkBAAAA3MycLpwsFouGDx9e5C+0jYiIUERERDlPBQAAAKAy\nc6pL9QAAAADAGRFOAAAAAGBAOAEAAACAAeEEAAAAAAaEEwAAAAAYEE4AAAAAYEA4AQAAAIAB4QQA\nAAAABoQTAAAAABgQTgAAAABgQDgBAAAAgAHhBAAAAAAGhBMAAAAAGBBOAAAAAGBAOAEAAACAAeEE\nAAAAAAaEEwAAAAAYEE4AAAAAYEA4AQAAAIAB4QQAAAAABoQTAAAAABgQTgAAAABgQDgBAAAAgAHh\nBAAAAAAGhBMAAAAAGBBOAAAAAGBAOAEAAACAAeEEAAAAAAaEEwAAAAAYEE4AAAAAYEA4AQAAAIAB\n4QQAAAAABoQTAAAAABgQTgAAAABgQDgBAAAAgAHhBAAAAAAGhBMAAAAAGBBOAAAAAGBAOAEAAACA\ngVOHU3Jyspo3b66EhAT7svXr16tnz54KCAhQRESEZs6cqZycnAqcEgAAAMDNzrWiByjKpUuX9OKL\nL6p69er2ZV9++aXGjh2rN954Q507d9ZPP/2kmJgYVa1aVcOHD6/AaQEAAADczJz2jNOMGTPUqFEj\nNW/e3L5s2bJlCg8PV/fu3eXm5iZfX18NGjRISUlJys3NrcBpAQAAANzMnDKc9uzZo7Vr1+p///d/\nHZanpKTI39/fYZm/v79sNpt+/vnncpwQAAAAQGXidJfqXbx4US+++KLi4uJUt25dh3Xp6eny9PR0\nWGa1Wu3rGjdubHz85OTk0hsWAGDE910AwNVuxJ8NThdOM2bM0J133qlevXqVyeMHBgaWyeNet7Vb\nK3oCACgTTvt918m9++mKih4BAMqMM/9sKCrqnCqc8i/R++ijjwpd7+XlJZvN5rAsIyNDkuTt7V3m\n8wEAAAConJwqnN5//3399ttveuSRR+zLMjMztXfvXn3yyScKCAhQamqqwz7Jycny9vaWj49PeY8L\nAAAAoJJwqnAaO3asRo0a5bBs1KhRatOmjYYOHapjx47pr3/9qzZu3KguXbro4MGDWrx4sYYMGSKL\nxVJBUwMAAAC42TlVOHl6eha4+YObm5vc3d3l7e0tb29vzZgxQ/Hx8YqNjZWXl5eioqI0ZMiQCpoY\nAAAAQGXgVOFUmKSkJIc/R0REKCIiooKmAQAAAFAZOeXvcQIAAAAAZ0I4AQAAAIAB4QQAAAAABoQT\nAAAAABgQTgAAAABgQDgBAAAAgAHhBAAAAAAGhBMAAAAAGBBOAAAAAGBAOAEAAACAAeEEAAAAAAaE\nEwAAAAAYEE4AAAAAYEA4AQAAAIAB4QQAAAAABoQTAAAAABgQTgAAAABgQDgBAAAAgAHhBAAAAAAG\nhBMAAAAAGBBOAAAAAGBAOAEAAACAAeEEAAAAAAaEEwAAAAAYEE4AAAAAYEA4AQAAAIAB4QQAAAAA\nBoQTAAAAABgQTgAAAABgQDgBAAAAgAHhBAAAAAAGhBMAAAAAGBBOAAAAAGBAOAEAAACAAeEEAAAA\nAAaEEwAAAAAYEE4AAAAAYEA4AQAAAIAB4QQAAAAABk4XTmfOnNG4cePUsWNHtW3bVn369NEXX3xh\nX79+/Xr17NlTAQEBioiI0MyZM5WTk1OBEwMAAAC42TldOD399NM6deqUPvjgA33xxRcKCQnR008/\nrZMnT+rLL7/U2LFjNWzYMO3evVsJCQlat26d5s6dW9FjAwAAALiJOVU4nT9/Xk2aNNGLL74ob29v\nVatWTdHR0frtt9+0d+9eLVu2TOHh4erevbvc3Nzk6+urQYMGKSkpSbm5uRU9PgAAAICblFOFU40a\nNfTaa6+pSZMm9mVpaWmSpNtvv10pKSny9/d32Mff3182m00///xzeY4KAAAAoBJxqnC6WmZmpsaN\nG6fOnTvLz89P6enp8vT0dNjGarVKktLT0ytiRAAAAACVgGtFD1CUY8eOKSYmRl5eXpo2bVqpPW5y\ncnKpPRYAwIzvuwCAq92IPxucMpz27t2rmJgYRUREaPz48apataokycvLSzabzWHbjIwMSZK3t3eJ\nHjswMLB0hy0ta7dW9AQAUCac9vuuk3v30xUVPQIAlBln/tlQVNQ5XTgdOnRI0dHReuqppzRo0CCH\ndQEBAUpNTXVYlpycLG9vb/n4+JTjlAAAAAAqE6f6jFNOTo7Gjh2r3r17F4gmSXriiSe0c+dObdy4\nUVlZWdq3b58WL16swYMHy2KxlP/AAAAAACoFpzrj9M033+jAgQM6dOiQli5d6rAuMjJSkydP1owZ\nMxQfH6/Y2Fh5eXkpKipKQ4YMqaCJAQAAAFQGThVOQUFBOnjwYLHbREREKCIiopwmAgAAAAAnu1QP\nAAAAAJwR4QQAAAAABoQTAAAAABgQTgAAAABgQDgBAAAAgAHhBAAAAAAGhBMAAAAAGBBOAAAAAGBA\nOAEAAACAAeEEAAAAAAaEEwAAAAAYEE4AAAAAYEA4AQAAAIAB4QQAAAAABoQTAAAAABgQTgAAAABg\nQDgBAAAAgAHhBAAAAAAGhBMAAAAAGBBOAAAAAGBAOAEAAACAAeEEAAAAAAaEEwAAAAAYEE4AAAAA\nYEA4AQAAAIAB4QQAAAAABoQTAAAAABgQTgAAAABgQDgBAAAAgAHhBAAAAAAGhBMAAAAAGBBOAAAA\nAGBAOAEAAACAAeEEAAAAAAaEEwAAAAAYEE4AAAAAYEA4AQAAAIAB4QQAAAAABoQTAAAAABgQTgAA\nAABgcEOG08WLFzVx4kR16tRJgYGB6tu3rz777LOKHgsAAADATeqGDKdJkybpm2++0aJFi/T555+r\nZ8+eiomJ0Y8//ljRowEAAAC4Cd1w4XT27Fl99NFHGjFihBo1aqRq1aqpX79+atKkid55552KHg8A\nAADATeiGC6cDBw4oOztbfn5+Dsv9/f2VmppaQVMBAAAAuJm5VvQA1yo9PV2SVLNmTYflVqtVZ86c\nMe6fnJxcJnNdrzGRHhU9AgCUCWf9vuvs+tQYUNEjAECZuRF/Ntxw4VQci8VS7PrAwMBymgQAAADA\nzeSGu1Svdu3akiSbzeawPCMjQ15eXhUxEgAAAICb3A0XTq1atZKbm5tSUlIcln/99dcKCgqqoKkA\nAAAA3MxuuHCqUaOGHnvsMSUkJOinn37SxYsXtWjRIh07dkz9+vWr6PEAAAAA3IQseXl5eRU9xLXK\nysrS1KlTtWHDBl24cEHNmzdXbGwsn2ECAAAAUCZuyHACAAAAgPJ0w12qB6D89e/fX2PHjpUkffjh\nh/Lz81NOTk4FTwUAKEtfffWV/Pz8lJaWVtGjAE6BcAKuEhUVpbvvvrvQ3y8wduxYe0D8GWvWrJGv\nr68uX75cYN17770nX1/fP/3YJbV161YdOHDgT+//6KOPat++fXJxcSnFqQDg5hAVFaXnn3++0HV/\n/EeosjR37lzl5uYWub5Tp05q0aKF/Pz81KpVK4WEhGjgwIF67733HPYLDg7Wvn371LBhwzKfGbgR\nEE5AIaxWq1566SVlZWVV9CilLiEhQf/5z38qegwAQBk4ePCgZs2aVWw4SVJ0dLT27dunffv2ad26\ndRowYIDmzZunYcOG3ZQ/+4DSQDgBhejdu7ckKTExsdjtjh07puHDh6tjx45q3bq1+vbtq927d5fK\nDDk5OZo9e7a6du2q1q1bq3Pnzlq4cKHDNh9++KEefvhhBQQEqGPHjnrppZd06dIlSdLRo0fl6+ur\nVatW6b777tOLL76o8PBwHThwQBMnTtQjjzxS6HHPnTun0aNHKzg4WB07dizwGlx91mzDhg32Gdq1\na6fhw4fr5MmT9u03bdqkXr16KSAgQKGhoZowYYIyMzPt61NTUxUVFaV27dopODhY0dHRDpeFfP75\n5+rdu7cCAwMVFBSkwYMH6/vvv7ev37VrlwYMGKCgoCAFBwfr2Wef1a+//vonX3UAKD9Hjx7V8OHD\n1aFDB7Vp00aPP/64UlNT7esvXLigl156Sffcc48CAgLUo0cPbdiwwb4+ISFBvXr10vTp09W2bVtt\n27ZNvXr1kiQFBPz/9u4+KKrqDeD4F5eVZYCEwLBExHwb/yBFVNBSAyFCRDMpCQZIQTEdNZXAtxRF\nJjPBTAPRHEcLQe1Fg0RzxkFAERVXzReaURRQHNTkJUhYl+X3B+OdNlahH/z69cfzmWGGOffsufcs\nw7Pz3HPus27s3Lmz3WswMzPD0dGRN998k71793LhwgV2794NQFFREYMHD6asrAzofDwuLS1l9uzZ\neHp64u7uTmhoqNEOiCtXrhAWFsbIkSNxc3MjODiYc+fOKcdLSkqYOXMmHh4euLm5MWvWLG7evPl3\n33Yh/muSOAlhglqtZu3atWzfvp0bN26Y7KPX65k5cyZqtZqsrCyKiorw8PBg9uzZ3Llzp9PXsHXr\nVg4ePMgXX3zB+fPn+fTTT0lNTeXgwYMA/PLLL8TFxbF48WK0Wi179+7l+PHjbRKdQ4cOkZmZSWJi\nInl5eQDEx8fz448/mjzv+vXruXbtGt9//z3Hjh2jtraWkpISk32rqqr46KOPiImJ4fz58xw9ehSA\nDRs2AK0fsnFxccydO5dz586xb98+Ll++TGJiItBaIXP27NkMHTqUU6dOcfz4cZqbm1m2bBkAjx8/\nZt68eUybNo0zZ86Qm5tLv379WLlyJQDXr18nOjqayZMnU1hYyOHDh6mrq2PJkiWdeeuFEOJ/TqfT\nMWPGDGxtbTly5AiFhYW4u7sTFRWl3FxKTk6muLiYH374gXPnzhEWFkZsbCy3bt1Sxrlz5w56vZ7T\np08zbtw4EhISANBqtURGRv6ta3J0dCQwMNDk50NXxOOFCxfSo0cPcnNzOXnyJE5OTsyfP185vmTJ\nEoYPH87Jkyc5ffo0Xl5exMTE0NzczMOHD4mIiGDYsGGcOHGCEydOYG9vT3R0tDxzK/4xkjgJ8RTu\n7u68/fbbrFy5ElPFJ/Pz8ykrK2PlypXY2dmh0WiYP38+Go2Gw4cPP3NsNzc3XF1djX7i4+OV4waD\ngb179zJr1iwGDx6MSqVixIgRvPPOO+zfvx9o/TLowsJCvLy8AHB2dsbd3d3obiWAv78/vXr1wszM\nrEPzzsnJISQkhD59+mBpacnChQtRq9Um+9bX19Pc3IylpSVmZmbY2dmxZcsWkpKSAEhPT8fX1xcf\nHx9UKhXOzs7Mnz+frKwsGhsb6d69O8eOHWPBggWYm5tjY2PDhAkTlDnodDqampqwsLBApVJhbW3N\nxx9/TGZmJgD79+9nyJAhBAcHo1ar6dmzJ7GxsRQVFVFeXt6h+QohRFf76aef2sR4V1dXtFqt0icv\nL4/KykqWL1+OjY0NlpaWLFq0CJVKRU5ODgBxcXFkZmbi4OCASqViypQp6PV6o1Wauro65s6dS/fu\n3Tsc55+lf//+JuNnV8TjjIwMEhIS0Gg0aDQaJk6cyJ07d5RVqd9//x21Wo1arcbCwoLo6Ghyc3NR\nqVRkZWWhVqtZsGABGo2G5557juXLl1NRUcGZM2c6PW8hOsL8/30BQvybxcTE4O/vT0ZGBiEhIUbH\nysrKeP7557G3t1fa1Go1zs7O7VYg0mq1mJsb//sdOHBAuXP38OFDampqSEhIYN26dUqflpYWevbs\nCbQmV3v27CE7O5t79+7R0tKCXq9nxIgRRuM6Ozt3eL7V1dX88ccfODk5KW3du3enb9++Jvv379+f\n8PBw3n//fQYNGoSnpyf+/v4MHToUaN2WUVZWxs8//2z0OoPBQFVVFX379iU3N5ddu3Zx69Yt9Ho9\nBoNB2QZoZWXF4sWLWbVqFWlpaYwePRpfX1/GjBmjjH/x4kVcXV2NxlepVNy+fftvzV0IIbpKQEAA\nGzdubNP+3nvvKb+Xlpai1+vx8PAw6mMwGJRdC3fv3mXDhg0UFxdTX1+vJEZNTU1Kf1tbW2xsbLrs\n2vV6vcniP10Rj7VaLV9++SXXr1+nqalJuSn5ZD6xsbGsXbuW7777jtGjR+Pt7Y2XlxcqlYrS0lIe\nPHjQZvxu3bpx+/btLpu/EM8iiZMQz2Btbc2qVatYunQpEyZMMDqm0+lMrkS190BuR2g0GgA2bdqE\nr6+vyT6pqans2bOHzZs34+npiVqtZvHixW2e73naapEpTx4I7tbNeDH6WXNasWIFUVFRFBQUkJeX\nR2hoKJGRkSxatAiNRkNISIiSEP5VUVERsbGxxMXF8e6772JlZUVmZiarV69W+kRFRREUFMTJkyfJ\nz89n3rx5eHt7k5SUhEaj4fXXXyc1NbXDcxRCiH8DjUaDtbW1yQqu0Bp3IyMj6d27N99++y29e/fm\n8ePHbRKHvxPjO+Lq1asMHDjQ5LHOxOObN2/ywQcfEBYWxrZt27C1tSU/P5+oqCilz5QpU/Dx8aGw\nsJCCggJWrFjBwIED2b17NxqNhkGDBj11m7kQ/wTZqidEO3x9ffH09GTt2rVG7S4uLlRXV3Pv3j2l\nTafTUV5ezssvv9ypc1pbW+Pg4NCm+l1VVZWS3Gi1WkaNGsXYsWNRq9UYDAYuX77cqfPa29ujVqup\nrKxU2nQ6nfJg8F8ZDAZqampwdHRk2rRpbN68mdWrV/P1118Dre/RtWvXjF5TV1dHTU0N0FoYwsrK\nihkzZmBlZaW0/dnDh8iONXsAAAR4SURBVA+xtbUlICCA9evXk5KSQnZ2NjU1Nbi4uPDrr78aJXZN\nTU1GxSmEEOLfyMXFhfr6+jbb4p7sWPjtt9+oqKggNDQUJycnzMzM2sTHrlZWVsaRI0eUAhN/1Zl4\nfPXqVR4/fkx0dDS2traA6XhvZWWFj48P8fHxHDhwgLNnz1JSUoKLiwvl5eVGxYVaWlrkO6bEP0oS\nJyE6YNWqVZw+fZpTp04pbePHj+fFF19k3bp11NXV0dDQwMaNGzEYDEycOLHT54yIiCA9PZ3CwkKa\nm5spKSkhJCREqZLk7OxMaWkp1dXVPHjwgDVr1mBjY8O9e/dMfk/UE5aWlty8eZPa2to2x8zNzRk/\nfjzp6elUVlbS0NBAcnLyU1ecsrOzmTRpEpcuXaKlpYWGhgYuX76sJI7h4eEUFxeTnp5OY2Mj9+/f\nJyYmhkWLFgHQp08fHj16xJUrV2hoaCAjI0OpkFRZWUlxcTETJkygoKCA5uZmdDodFy5cwMHBgR49\nehAcHMz9+/f5/PPPqa+vp7a2ljVr1hAREdElK39CCPG/8uqrrzJgwADi4+OVm2IZGRlMnDiRiooK\n7OzssLa2RqvVotfruXTpErt27cLKysro5tZfWVpaAq3FGv6cZDxLY2Mjx44dIyIigrFjxyqVZf+s\ns/H4yXdBFRcX09TURE5ODmfPngVatyRWVlYybtw4srKy0Ol06PV6iouLsbCw4KWXXiIwMBBLS0sS\nEhKorq7m0aNHbN68maCgoA7PU4jOksRJiA5wdHRkyZIlRisZFhYW7Ny5k8bGRvz8/PD29ubGjRtk\nZGTwwgsvdPqckZGRhIaGsmzZMoYNG8a8efOYOnUq0dHRAMyZM4devXrh5eXF9OnTcXNzY8WKFdTW\n1hIYGPjUccPCwvjmm28ICAgweTwhIYF+/foxefJk/Pz86NGjR5vnpp4IDAwkNDSUDz/8UCmZ/uDB\nA5KTk4HWIhhJSUlkZmYycuRI3nrrLezs7JTiEW+88QZTp04lPDwcHx8fKioqSElJYcCAAUyaNAkH\nBweWLl1KYmIiw4cPZ+zYsZw5c4Zt27ZhZmaGk5MTaWlpFBYWMmbMGPz8/KitrWXHjh1tthsKIcS/\niUqlYtu2bVhYWODv78/o0aM5dOgQ27dvp0+fPpibm/PJJ59w9OhRRowYwWeffcbSpUuZPn06aWlp\npKWlmRx3zJgxDBkyhKCgIFJSUp56/h07dihFKzw9PUlLSyM6OpotW7aYjJ/u7u6disevvPIKc+bM\nYfny5bz22mvk5eWxdetW3N3dmTVrFrdv32bTpk3s3LmTUaNG4enpyb59+0hNTVWSyK+++oq7d+/i\n5eXFuHHjuHjxIrt27cLa2rrL/i5CPItZi6mHNIQQQgghhBBCKOSWrBBCCCGEEEK0QxInIYQQQggh\nhGiHJE5CCCGEEEII0Q5JnIQQQgghhBCiHZI4CSGEEEIIIUQ7JHESQgghhBBCiHZI4iSEEEIIIYQQ\n7ZDESQghhBBCCCHaIYmTEEIIIYQQQrTjP7M4H6vyR2NiAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Z_VTHdoWEs2",
        "colab_type": "code",
        "outputId": "85119257-1742-4d64-b522-c5a7664b2567",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        }
      },
      "source": [
        "f = sns.countplot(x='target', data=data, hue='sex')\n",
        "plt.legend(['Female', 'Male'])\n",
        "f.set_title(\"Heart disease presence by gender\")\n",
        "f.set_xticklabels(['No Heart disease', 'Heart Disease'])\n",
        "plt.xlabel(\"\");"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data[hue_mask])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ABUyeBqfOnTurVq1amfbt3LlTXl5edm1eXl5KSkrSsWPHFB8fryJFiujxxx+3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\nEEIIIYQQPZDESQghhBBCCCF68D+BbNwxKS6ylgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjgW6iriUQgp",
        "colab_type": "code",
        "outputId": "ba24e5ee-a5ef-495f-99d0-24a883389cf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "source": [
        "heat_map = sns.heatmap(data.corr(method='pearson'), annot=True, fmt='.2f', linewidths=2)\n",
        "heat_map.set_xticklabels(heat_map.get_xticklabels(), rotation=45);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QEFcPT+5EhZOanAiVCv+S4b9C2LnpI6yDBw8eEBQURN26dTl58iRRUVGsWrUK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9AFixYgU7duzA1NSUS5cukZOTw7Jlyzh58iRZWVnY2dnx+eefM2hQ8eTOP//8k5UrVxIXF4eD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6JXOn8EITox4ytqojkVbc0Ux+/ev/+KmmiOsZGR4jgxvfA1xf8t2JqbKI5z4p9/RU00R9++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\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jf7YfxMOT53lwLJBVbQci1pTQfPKwzHotf/sZkUTCipZ9uXvkNI/PXGJd55+ICwun7ayx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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOhxXB1kfD4u",
        "colab_type": "code",
        "outputId": "c3dec441-0579-45a6-f6e7-2809cf464ccc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        }
      },
      "source": [
        "plt.scatter(x=data.age[data.target==1], y=data.thalach[(data.target==1)], c=\"red\", s=60)\n",
        "plt.scatter(x=data.age[data.target==0], y=data.thalach[(data.target==0)], s=60)\n",
        "plt.legend([\"Disease\", \"No Disease\"])\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Maximum Heart Rate\");"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IoepqozEmpmldO9N/MTFGo15vfd1ly5pfr+G/5csdv90xY2xvc+xYseejhfGe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LjY1V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\nAAA4QeMEAAAAAE7QOAEAAACAE/8fGwKz4XSNZE4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "swSuoLbBZUZZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['Age_Category'] = pd.cut(data['age'],bins=list(np.arange(25, 85, 5)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkUiEKEwZP1v",
        "colab_type": "code",
        "outputId": "dddb4c2a-41eb-4a96-90d4-b7cd1158ba04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        }
      },
      "source": [
        "plt.subplot(121)\n",
        "data[data['target']==1].groupby('Age_Category')['age'].count().plot(kind='bar')\n",
        "plt.title('Age Distribution of Patients with +ve Heart Diagonsis')\n",
        "\n",
        "plt.subplot(122)\n",
        "data[data['target']==0].groupby('Age_Category')['age'].count().plot(kind='bar')\n",
        "plt.title('Age Distribution of Patients with -ve Heart Diagonsis')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Age Distribution of Patients with -ve Heart Diagonsis')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D7c7N+8Sd1zpIt88e/f3333e9f9/L9r97926NGjXKqg9wdHS0DEnKbpgbfRZ9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\nQIQuAAAAADAQoQsAUCjFxMS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H69FHH3V1WAAAAACwBacXAgAAAICFXN50ffHF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\nRNMFAAAAABb6f49cCkA2TnhyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBoXX2pbRxiF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del data['Age_Category']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mz7TLvNOZjsR",
        "colab_type": "code",
        "outputId": "67719a31-75c3-4347-91a7-dbf30dae8ddd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        }
      },
      "source": [
        "f = sns.countplot(x='cp', data=data, hue='target')\n",
        "f.set_xticklabels(['Typical Angina', 'Atypical Angina', 'Non-anginal Pain', 'Asymptomatic']);\n",
        "f.set_title('Disease presence by chest pain type')\n",
        "plt.ylabel('Chest Pain Type')\n",
        "plt.xlabel('')\n",
        "plt.legend(['No Disease', 'Disease']);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data[hue_mask])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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URESEvvrqK/Xp00dNmzbV4sWLVb9+fR06dEgTJkzQ0aNHlZGRoapVq2rIkCGq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\nGVqEDKDvAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imlD2bj3-CQQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = data.loc[:,data.columns!='target']\n",
        "y = data.iloc[:,-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bhbi322tjnD7",
        "colab_type": "code",
        "outputId": "798a7510-50b7-45f2-d724-42de886afe45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVUWjm-mEQMO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature_columns = []\n",
        "\n",
        "# numeric cols\n",
        "for header in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'ca']:\n",
        "  feature_columns.append(tf.feature_column.numeric_column(header))\n",
        "\n",
        "# bucketized cols\n",
        "age = tf.feature_column.numeric_column(\"age\")\n",
        "age_buckets = tf.feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])\n",
        "feature_columns.append(age_buckets)\n",
        "\n",
        "# indicator cols\n",
        "data[\"thal\"] = data[\"thal\"].apply(str)\n",
        "thal = tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "      'thal', ['3', '6', '7'])\n",
        "thal_one_hot = tf.feature_column.indicator_column(thal)\n",
        "feature_columns.append(thal_one_hot)\n",
        "\n",
        "data[\"sex\"] = data[\"sex\"].apply(str)\n",
        "sex = tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "      'sex', ['0', '1'])\n",
        "sex_one_hot = tf.feature_column.indicator_column(sex)\n",
        "feature_columns.append(sex_one_hot)\n",
        "\n",
        "data[\"cp\"] = data[\"cp\"].apply(str)\n",
        "cp = tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "      'cp', ['0', '1', '2', '3'])\n",
        "cp_one_hot = tf.feature_column.indicator_column(cp)\n",
        "feature_columns.append(cp_one_hot)\n",
        "\n",
        "data[\"slope\"] = data[\"slope\"].apply(str)\n",
        "slope = tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "      'slope', ['0', '1', '2'])\n",
        "slope_one_hot = tf.feature_column.indicator_column(slope)\n",
        "feature_columns.append(slope_one_hot)\n",
        "\n",
        "\n",
        "# embedding cols\n",
        "thal_embedding = tf.feature_column.embedding_column(thal, dimension=8)\n",
        "feature_columns.append(thal_embedding)\n",
        "\n",
        "# crossed cols\n",
        "age_thal_crossed = tf.feature_column.crossed_column([age_buckets, thal], hash_bucket_size=1000)\n",
        "age_thal_crossed = tf.feature_column.indicator_column(age_thal_crossed)\n",
        "feature_columns.append(age_thal_crossed)\n",
        "\n",
        "cp_slope_crossed = tf.feature_column.crossed_column([cp, slope], hash_bucket_size=1000)\n",
        "cp_slope_crossed = tf.feature_column.indicator_column(cp_slope_crossed)\n",
        "feature_columns.append(cp_slope_crossed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRHM4UnxcLQj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_dataset(dataframe, batch_size=32):\n",
        "  dataframe = dataframe.copy()\n",
        "  labels = dataframe.pop('target')\n",
        "  return tf.data.Dataset.from_tensor_slices((dict(dataframe), labels)) \\\n",
        "          .shuffle(buffer_size=len(dataframe)) \\\n",
        "          .batch(batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbxq2bzFcRUs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train, test = train_test_split(data, test_size=0.2, random_state=RANDOM_SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DW0afy46cWIH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_ds = create_dataset(train)\n",
        "test_ds = create_dataset(test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIgmYKbGElvj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.DenseFeatures(feature_columns=feature_columns),\n",
        "  tf.keras.layers.Dense(units=128, activation='relu'),\n",
        "  tf.keras.layers.Dropout(rate=0.2),\n",
        "  tf.keras.layers.Dense(units=128, activation='relu'),\n",
        "  tf.keras.layers.Dense(units=1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62BvIGBLXgJN",
        "colab_type": "code",
        "outputId": "9e09d7cc-1f99-4b4e-a72a-374975952ce0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3709
        }
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_ds, validation_data=test_ds, epochs=100, use_multiprocessing=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0411 17:50:01.978150 139956992104320 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/feature_column/feature_column_v2.py:3048: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n",
            "W0411 17:50:01.986980 139956992104320 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/feature_column/feature_column_v2.py:2758: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0411 17:50:02.000078 139956992104320 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/feature_column/feature_column_v2.py:2902: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0411 17:50:02.058870 139956992104320 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/feature_column/feature_column_v2.py:4307: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n",
            "W0411 17:50:02.060064 139956992104320 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/feature_column/feature_column_v2.py:4362: CrossedColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 1s 106ms/step - loss: 2.9316 - accuracy: 0.5077 - val_loss: 1.9441 - val_accuracy: 0.4754\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 1.9688 - accuracy: 0.4497 - val_loss: 0.8471 - val_accuracy: 0.5246\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 1.2230 - accuracy: 0.5871 - val_loss: 1.0657 - val_accuracy: 0.5082\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 1.1199 - accuracy: 0.4796 - val_loss: 0.5146 - val_accuracy: 0.7213\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.7200 - accuracy: 0.6755 - val_loss: 0.5666 - val_accuracy: 0.7213\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.7670 - accuracy: 0.6083 - val_loss: 0.4559 - val_accuracy: 0.8361\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.7675 - accuracy: 0.6097 - val_loss: 0.4881 - val_accuracy: 0.7869\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.7677 - accuracy: 0.6435 - val_loss: 0.4880 - val_accuracy: 0.8033\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.6691 - accuracy: 0.6244 - val_loss: 0.4691 - val_accuracy: 0.8033\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 0.7221 - accuracy: 0.6334 - val_loss: 0.5353 - val_accuracy: 0.7541\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 0.6755 - accuracy: 0.6507 - val_loss: 0.4570 - val_accuracy: 0.8361\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 0.6103 - accuracy: 0.7031 - val_loss: 0.4760 - val_accuracy: 0.8033\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.6593 - accuracy: 0.6680 - val_loss: 0.5086 - val_accuracy: 0.7541\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.6461 - accuracy: 0.6336 - val_loss: 0.4363 - val_accuracy: 0.8197\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.5876 - accuracy: 0.6994 - val_loss: 0.4516 - val_accuracy: 0.8361\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.5263 - accuracy: 0.7696 - val_loss: 0.4448 - val_accuracy: 0.8197\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 0.5436 - accuracy: 0.6982 - val_loss: 0.4097 - val_accuracy: 0.8361\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.5434 - accuracy: 0.7554 - val_loss: 0.4242 - val_accuracy: 0.8197\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.5281 - accuracy: 0.7421 - val_loss: 0.4286 - val_accuracy: 0.8197\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.5824 - accuracy: 0.6493 - val_loss: 0.4033 - val_accuracy: 0.8525\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.4995 - accuracy: 0.7411 - val_loss: 0.3758 - val_accuracy: 0.8852\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.5268 - accuracy: 0.7431 - val_loss: 0.4210 - val_accuracy: 0.8525\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.5531 - accuracy: 0.6954 - val_loss: 0.3801 - val_accuracy: 0.8852\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.5047 - accuracy: 0.7234 - val_loss: 0.3805 - val_accuracy: 0.8852\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 0.4991 - accuracy: 0.8044 - val_loss: 0.3793 - val_accuracy: 0.8852\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 0.5179 - accuracy: 0.6790 - val_loss: 0.3868 - val_accuracy: 0.8852\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 0.5107 - accuracy: 0.7693 - val_loss: 0.3900 - val_accuracy: 0.8689\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 0.4877 - accuracy: 0.7480 - val_loss: 0.3425 - val_accuracy: 0.8852\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.4508 - accuracy: 0.7774 - val_loss: 0.3695 - val_accuracy: 0.8689\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.4112 - accuracy: 0.8215 - val_loss: 0.3569 - val_accuracy: 0.8689\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 0.4815 - accuracy: 0.7384 - val_loss: 0.3371 - val_accuracy: 0.8852\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 0.4393 - accuracy: 0.8228 - val_loss: 0.3536 - val_accuracy: 0.8852\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 0.4564 - accuracy: 0.7930 - val_loss: 0.3384 - val_accuracy: 0.8689\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 0.4366 - accuracy: 0.8003 - val_loss: 0.3378 - val_accuracy: 0.8689\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.4186 - accuracy: 0.7998 - val_loss: 0.3676 - val_accuracy: 0.8852\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.4346 - accuracy: 0.7692 - val_loss: 0.3476 - val_accuracy: 0.8689\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.4586 - accuracy: 0.7572 - val_loss: 0.3460 - val_accuracy: 0.8689\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.4158 - accuracy: 0.7983 - val_loss: 0.3702 - val_accuracy: 0.8852\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3980 - accuracy: 0.7915 - val_loss: 0.3187 - val_accuracy: 0.9016\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3968 - accuracy: 0.8391 - val_loss: 0.3642 - val_accuracy: 0.8852\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.4162 - accuracy: 0.7870 - val_loss: 0.3423 - val_accuracy: 0.8689\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.4083 - accuracy: 0.7976 - val_loss: 0.3465 - val_accuracy: 0.8689\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.4397 - accuracy: 0.7677 - val_loss: 0.3596 - val_accuracy: 0.9016\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.4053 - accuracy: 0.8095 - val_loss: 0.3344 - val_accuracy: 0.9016\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.4153 - accuracy: 0.8407 - val_loss: 0.3313 - val_accuracy: 0.9016\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3895 - accuracy: 0.8248 - val_loss: 0.3581 - val_accuracy: 0.9016\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.4048 - accuracy: 0.8291 - val_loss: 0.3308 - val_accuracy: 0.9016\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3942 - accuracy: 0.8268 - val_loss: 0.3589 - val_accuracy: 0.8852\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3998 - accuracy: 0.8298 - val_loss: 0.3686 - val_accuracy: 0.9016\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3756 - accuracy: 0.8085 - val_loss: 0.3454 - val_accuracy: 0.8689\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3767 - accuracy: 0.8058 - val_loss: 0.3577 - val_accuracy: 0.9016\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3844 - accuracy: 0.8098 - val_loss: 0.3381 - val_accuracy: 0.8852\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 0.3870 - accuracy: 0.8270 - val_loss: 0.3501 - val_accuracy: 0.8852\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 0.3704 - accuracy: 0.8282 - val_loss: 0.3430 - val_accuracy: 0.8852\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3725 - accuracy: 0.7970 - val_loss: 0.3804 - val_accuracy: 0.8852\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3787 - accuracy: 0.8185 - val_loss: 0.3692 - val_accuracy: 0.8689\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3773 - accuracy: 0.8222 - val_loss: 0.3511 - val_accuracy: 0.8852\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3819 - accuracy: 0.8382 - val_loss: 0.3614 - val_accuracy: 0.8852\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 0.3709 - accuracy: 0.8477 - val_loss: 0.3598 - val_accuracy: 0.8852\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3535 - accuracy: 0.8502 - val_loss: 0.3553 - val_accuracy: 0.8852\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 0.3322 - accuracy: 0.8530 - val_loss: 0.3528 - val_accuracy: 0.8852\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3599 - accuracy: 0.8349 - val_loss: 0.3736 - val_accuracy: 0.8852\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3703 - accuracy: 0.8237 - val_loss: 0.3579 - val_accuracy: 0.8852\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3342 - accuracy: 0.8451 - val_loss: 0.3290 - val_accuracy: 0.8852\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3440 - accuracy: 0.8235 - val_loss: 0.3618 - val_accuracy: 0.9016\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3658 - accuracy: 0.8145 - val_loss: 0.3438 - val_accuracy: 0.8852\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3653 - accuracy: 0.8308 - val_loss: 0.3283 - val_accuracy: 0.8689\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3349 - accuracy: 0.8349 - val_loss: 0.3491 - val_accuracy: 0.8852\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3609 - accuracy: 0.8410 - val_loss: 0.3857 - val_accuracy: 0.8852\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3527 - accuracy: 0.8136 - val_loss: 0.3240 - val_accuracy: 0.8689\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3488 - accuracy: 0.8283 - val_loss: 0.3675 - val_accuracy: 0.8852\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3471 - accuracy: 0.8144 - val_loss: 0.3900 - val_accuracy: 0.8852\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3580 - accuracy: 0.8301 - val_loss: 0.3436 - val_accuracy: 0.8852\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3300 - accuracy: 0.8660 - val_loss: 0.3634 - val_accuracy: 0.8852\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3283 - accuracy: 0.8488 - val_loss: 0.3400 - val_accuracy: 0.8852\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3512 - accuracy: 0.8119 - val_loss: 0.3425 - val_accuracy: 0.8852\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3237 - accuracy: 0.8535 - val_loss: 0.3470 - val_accuracy: 0.8852\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3302 - accuracy: 0.8573 - val_loss: 0.3554 - val_accuracy: 0.8852\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3526 - accuracy: 0.8300 - val_loss: 0.3503 - val_accuracy: 0.8852\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3233 - accuracy: 0.8405 - val_loss: 0.3399 - val_accuracy: 0.8852\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3313 - accuracy: 0.8475 - val_loss: 0.3565 - val_accuracy: 0.8852\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3119 - accuracy: 0.8654 - val_loss: 0.3538 - val_accuracy: 0.8852\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3154 - accuracy: 0.8675 - val_loss: 0.3699 - val_accuracy: 0.8852\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3328 - accuracy: 0.8748 - val_loss: 0.3484 - val_accuracy: 0.8852\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3013 - accuracy: 0.8564 - val_loss: 0.3551 - val_accuracy: 0.8852\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3518 - accuracy: 0.8638 - val_loss: 0.3789 - val_accuracy: 0.8852\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3412 - accuracy: 0.8385 - val_loss: 0.3833 - val_accuracy: 0.8852\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3106 - accuracy: 0.8706 - val_loss: 0.3493 - val_accuracy: 0.8852\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3311 - accuracy: 0.8413 - val_loss: 0.3402 - val_accuracy: 0.8852\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3365 - accuracy: 0.8426 - val_loss: 0.3484 - val_accuracy: 0.8852\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3230 - accuracy: 0.8623 - val_loss: 0.3581 - val_accuracy: 0.8852\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3215 - accuracy: 0.8393 - val_loss: 0.3742 - val_accuracy: 0.8852\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3097 - accuracy: 0.8566 - val_loss: 0.3740 - val_accuracy: 0.8852\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3149 - accuracy: 0.8609 - val_loss: 0.3463 - val_accuracy: 0.8852\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.2853 - accuracy: 0.8594 - val_loss: 0.3863 - val_accuracy: 0.8852\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 0.3135 - accuracy: 0.8593 - val_loss: 0.3786 - val_accuracy: 0.8852\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 0s 48ms/step - loss: 0.3134 - accuracy: 0.8802 - val_loss: 0.3279 - val_accuracy: 0.8852\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3098 - accuracy: 0.8592 - val_loss: 0.3651 - val_accuracy: 0.8852\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.3062 - accuracy: 0.8723 - val_loss: 0.3586 - val_accuracy: 0.8852\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 0.2828 - accuracy: 0.8567 - val_loss: 0.3431 - val_accuracy: 0.8852\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QG6sJRD8uqyJ",
        "colab_type": "code",
        "outputId": "544c8903-b9e2-4eed-dd09-e5973ddd9ac0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "model.evaluate(test_ds)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2/2 [==============================] - 0s 23ms/step - loss: 0.3431 - accuracy: 0.8852\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3430721387267113, 0.8852459]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HyDuGIwnwE1r",
        "colab_type": "code",
        "outputId": "25f14e95-5f88-40de-c949-2c036d1d01e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylim((0, 1))\n",
        "plt.legend(['train', 'test'], loc='upper left');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NnDiRiRMnVnn/wIEDGThwYIXb5syZw5w5c8r/P336dAC8vLy4ePFihW0ru00IIYQQQoh7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kpKfF0e/ExHR4ciIyOVl5enUaNGyWq1Kjs7W+fPn9fRo0e1detWzZgxQ/fee68sFosmTpyozMxM\nvfbaa+rt7fV1+fATBQUFstlsioqKco/ROxjMSy+9pKysLE2dOlUWi0VJSUl6//33Zbfb6R8MqKWl\nRU1NTbrvvvsUGhoqi8WiBQsWyOVyqa6uTlu2bFFqaqomT54si8WixMREpaamavPmzb4uHT7Q1tam\n119/XXPnzu03N9hx5vjx4zp06JBycnI0evRoDR8+XI888ogCAgK0a9cuj9ZNUPKQ2tpauVwuxcTE\n9BmPjY1VVVWVj6qCvwoJCdHatWv7PPa+sbFRkjR69GhVVlYqNja2z2tiY2PV1tamzz//3Julwk8d\nPnxY77zzjv70pz/1Gad3cCVnzpxRfX29goODNX/+fMXHx2vOnDnavXu3JPoHA4uIiFBCQoK2b9+u\nlpYWuVwubdu2TWFhYZo0aZLq6uou2zuffvqp++nGGDrS0tJks9kuOzfYcaaqqkpBQUH60Y9+5J4P\nDAxUdHS0x8+pAz367kNYS0uLJCk0NLTPeFhYmJqbm31REq4jDodDy5Yt06xZsxQTE6OWlhaNHDmy\nz5qwsDBJF3ttwoQJvigTfsLpdCovL09PPfWUbrrppj5z9A6u5NSpU5KkN998U3/+8581btw4bd++\nXU888YTGjBlD/+CKioqKlJ2drSlTpiggIEBhYWFav369ent71dPTc9ne6e3tVVtbm4YNG+ajquFv\nBjvOXJoPCAjosyY0NFT//ve/PVobV5R8wPwfDfynpqYmzZ8/X+Hh4Vq3bp2vy8F1oKCgQD/84Q/1\nwAMP+LoUXGeM//0LIZdusA4ODtaiRYtkt9tVWlrq4+rgz7q7u5WVlSWbzaZ9+/bp8OHDeuSRR7Rk\nyRL3joiBcB6Ea8XTvURQ8pDw8HBJF/dk/qfW1lZFRET4oiRcB44ePaq0tDQlJCTolVdeUXBwsKSL\nWxwu10uSNGrUKK/XCf9xacvdM888c9l5egdXcuONN0r6v9/eXjJ+/HidPn2a/sGADh48qGPHjrnv\nrb3hhhu0cOFC3Xzzzfrggw8UGBh42d4JDAzs128Y2gY7zoSHh+vcuXPuX+xc0tbW5vFzaoKSh9jt\ndlksFlVWVvYZr6ioUGJioo+qgj87fvy4srOz9dBDD2nlypUKCgpyz8XFxfXbh1teXq5Ro0Zp/Pjx\n3i4VfuTtt9/W+fPnlZKSoqSkJCUlJamiokIbN250P2qV3sFAbrzxRoWGhqq6urrP+BdffKGxY8fS\nPxjQpYd5mB8V39PTo+985zuXvX+kvLxcdrtdVqvVa3XC/w12nImLi5PL5VJtba17vru7W9XV1R4/\npyYoeUhISIhSU1NVVFSkhoYGOZ1OFRcXq6mpSenp6b4uD36mp6dHubm5SktLU2ZmZr/5X/7yl9q3\nb5/ee+8998Hh1Vdf1eLFi9nCMMTl5ubqww8/1DvvvOP+stvtSk9P1yuvvELv4Iq++93vavHixdq6\ndasOHDig7u5uvf766/rnP/+p+fPn0z8YUHx8vCIiIrRu3Tq1traqq6tLb731lhoaGpScnKzMzEyV\nlpaqrKxM3d3d2r9/v3bs2KHFixf7unT4mcGOM5GRkZoxY4aee+45nT59Wg6HQ+vWrZPVatXs2bM9\nWluAYb6OhWumu7tb+fn5evfdd9XZ2amoqCjl5OQoISHB16XBzxw+fFgLFy5UUFBQv5OPuXPnavXq\n1dq7d69eeOEFff7554qIiFB6eroefvhhTlbQT0ZGhiZNmqSlS5dKEr2DKzIMQy+99JL+/ve/q7m5\nWTabTU899ZSmTZsmif7BwOrq6lRQUKCamhp1dHRowoQJevTRRzVr1ixJUklJiTZu3KhTp07p+9//\nvrKzs5WWlubjquELP/3pT/Xll1/KMAy5XC73+c43Pcdpb2/X6tWr9dFHH8nlcikuLk5/+MMfdMst\nt3i0boISAAAAAJiw9Q4AAAAATAhKAAAAAGBCUAIAAAAAE4ISAAAAAJgQlAAAAADAhKAEAAAAACYE\nJQAABjFz5kzl5ub6ugwAgBcRlAAAAADAhKAEAAAAACYEJQCAXzIMQ6+++qpSUlJ02223adq0aVq9\nerXOnz8vScrNzdW9996rAwcOaM6cObLb7br77ru1c+fOPu9z/PhxPfTQQ0pISFBMTIxSUlJUWlra\nZ01HR4dWrFihO+64Q3FxcVqwYIE++eSTfjXt2LFDs2bNkt1u15w5c1RdXe25HwAAwKcISgAAv7Rh\nwwbl5+crJSVFu3bt0qpVq/TBBx8oJyfHvebMmTN6+eWXtWrVKu3cuVPx8fFatmyZjh49Kkk6e/as\nMjIy1NnZqU2bNmnXrl266667tGzZMu3Zs8f9PkuXLtWhQ4dUUFCgHTt2aMKECcrOzlZDQ4N7TVVV\nlQ4dOqSXX35ZW7ZsUVdXV59aAAD/XQJ9XQAAAGYul0vFxcWaO3eusrKyJEnjx49XR0eHcnJy9Nln\nn0mSHA6HcnJyZLfbJUlPP/203n//fb377ruKjY3V22+/LYfDofXr1ysiIkKS9Pjjj+vgwYPaunWr\nZs+ererqapWVlekvf/mLJk+eLElasWKFnE6nGhsbZbPZJEmdnZ165plnFBQUJEl64IEHVFhYqPb2\ndo0YMcKrPx8AgOcRlAAAfqe+vl4Oh0N33HFHn/EpU6ZIkmprayVJVqtV0dHR7vng4GDZbDY1NjZK\nkmpqajRu3Dh3SLokNjZWb731liS5t8/FxMS45y0Wi55//vk+r/nxj3/sDkmS9L3vfU/SxW17BCUA\n+O9DUAIA+B2HwyFJWr58uZ5++ul+82fPnpUkDR8+XAEBAX3mgoOD1d7e7n6fG264od/rhw8frgsX\nLujrr79WR0eHe+xKhg0b1ufflz7XMIxv8i0BAK4zBCUAgN8ZOXKkJOnJJ5/UjBkzLjv/7LPPyul0\n9pvr7OzU+PHjJUkhISH66quv+q1xOBwKDg5WYGCg+8pQe3v7oGEJADB08DAHAIDfsdlsGjFihL78\n8kv94Ac/cH+NGTNGvb29Cg0NlSQ5nU7V1NS4X3f+/Hk1NDQoMjJSkmS329XY2Oi+AnVJRUWFe6vd\npfubysvL3fO9vb369a9/re3bt3v0+wQA+C+CEgDA7wQGBiorK0tvvPGG3njjDX3xxReqra3Vk08+\nqfnz56utrU3SxW12zz77rI4cOaLPPvtMK1as0Ndff62UlBRJ0i9+8QuNGDFCjz/+uGpqalRfX681\na9bo2LFjys7OliRFRUVp6tSpys/P18cff6x//etfWrt2rQ4fPqz4+Hif/QwAAL7F1jsAgF96+OGH\nNWzYMG3ZskVr166V1WrV5MmTtXXrVvcVpeDgYC1ZskQrVqxQQ0ODRo8erfz8fPcVpfDwcL322mt6\n7rnntGjRIrlcLt1666168cUXNX36dPdnFRYWKj8/X48++qi6uro0ceJEFRcXa8KECT753gEAvhdg\ncBcqAOA6lJubq3/84x/av3+/r0sBAPwXYusdAAAAAJgQlAAAAADAhK13AAAAAGDCFSUAAAAAMCEo\nAQAAAIAJQQkAAAAATAhKAAAAAGBCUAIAAAAAE4ISAAAAAJj8D5q0kapYrg8kAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NaYh6u6Pw3Nk",
        "colab_type": "code",
        "outputId": "dcc39c0a-e783-4bd7-de5a-5cb5a500c85c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0wgAAAICfKw7NMUCap/rXXBkIm1wJAAAAgHgQlAwQPXS2vCpkciUAAAAA4kFQMkB6tKNU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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybqeTJyWxAPu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sp1rVWsIytrl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = model.predict(test_ds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfiKBFzyy3Kl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bin_predictions = tf.round(predictions).numpy().flatten()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNsRPiNsywLU",
        "colab_type": "code",
        "outputId": "6b78d688-896d-4cca-f12f-521606399521",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "print(classification_report(y_test.values, bin_predictions))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.66      0.62        29\n",
            "           1       0.66      0.59      0.62        32\n",
            "\n",
            "   micro avg       0.62      0.62      0.62        61\n",
            "   macro avg       0.62      0.62      0.62        61\n",
            "weighted avg       0.63      0.62      0.62        61\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3h4iXBqvy0WM",
        "colab_type": "code",
        "outputId": "46c13135-9fee-4dfd-f3a2-86008db7d642",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "cnf_matrix = confusion_matrix(y_test, bin_predictions)\n",
        "cnf_matrix"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[19, 10],\n",
              "       [13, 19]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zb5KUUmm0uoO",
        "colab_type": "code",
        "outputId": "7084ad95-0259-4dc3-d1cc-f35b889f7740",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        }
      },
      "source": [
        "class_names = [0,1]\n",
        "fig,ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks,class_names)\n",
        "plt.yticks(tick_marks,class_names)\n",
        "\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix),annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False)\n",
        "ax.xaxis.set_label_position('top')\n",
        "plt.tight_layout()\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Q1Z5ddtllmT59erUNAgAAgPqsSt+jvib9+vXLsGHDqmMLAAAA1Hv/dKh/8MEH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\nAAAKItQBAACgIEIdAAAACvL/AELznZVODV8/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}